Overview

Dataset statistics

Number of variables18
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows59
Duplicate rows (%)2.9%
Total size in memory267.7 KiB
Average record size in memory137.1 B

Variable types

Categorical4
Numeric13
Boolean1

Alerts

Dataset has 59 (2.9%) duplicate rowsDuplicates
artist has a high cardinality: 835 distinct values High cardinality
song has a high cardinality: 1879 distinct values High cardinality
genre has a high cardinality: 59 distinct values High cardinality
energy is highly correlated with loudness and 2 other fieldsHigh correlation
loudness is highly correlated with energy and 2 other fieldsHigh correlation
explicit is highly correlated with speechiness and 1 other fieldsHigh correlation
genre is highly correlated with explicit and 3 other fieldsHigh correlation
speechiness is highly correlated with explicitHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
instrumentalness is highly correlated with genreHigh correlation
song is uniformly distributed Uniform
popularity has 126 (6.3%) zeros Zeros
key has 198 (9.9%) zeros Zeros
instrumentalness has 1087 (54.4%) zeros Zeros

Reproduction

Analysis started2022-11-14 17:07:51.083575
Analysis finished2022-11-14 17:09:06.252533
Duration1 minute and 15.17 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

artist
Categorical

HIGH CARDINALITY

Distinct835
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Rihanna
 
25
Drake
 
23
Eminem
 
21
Calvin Harris
 
20
Britney Spears
 
19
Other values (830)
1892 

Length

Max length41
Median length22
Mean length9.9915
Min length2

Characters and Unicode

Total characters19983
Distinct characters84
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique494 ?
Unique (%)24.7%

Sample

1st rowBritney Spears
2nd rowblink-182
3rd rowFaith Hill
4th rowBon Jovi
5th row*NSYNC

Common Values

ValueCountFrequency (%)
Rihanna25
 
1.2%
Drake23
 
1.1%
Eminem21
 
1.1%
Calvin Harris20
 
1.0%
Britney Spears19
 
0.9%
David Guetta18
 
0.9%
Chris Brown17
 
0.9%
Kanye West17
 
0.9%
Beyoncé16
 
0.8%
Taylor Swift16
 
0.8%
Other values (825)1808
90.4%

Length

2022-11-14T18:09:06.563948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the85
 
2.4%
rihanna25
 
0.7%
david25
 
0.7%
lil23
 
0.7%
drake23
 
0.7%
22
 
0.6%
justin22
 
0.6%
eminem21
 
0.6%
j21
 
0.6%
calvin20
 
0.6%
Other values (1215)3226
91.8%

Most occurring characters

ValueCountFrequency (%)
e1744
 
8.7%
a1709
 
8.6%
1513
 
7.6%
i1388
 
6.9%
n1181
 
5.9%
r1038
 
5.2%
o973
 
4.9%
l958
 
4.8%
s845
 
4.2%
t649
 
3.2%
Other values (74)7985
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14349
71.8%
Uppercase Letter3821
 
19.1%
Space Separator1513
 
7.6%
Other Punctuation150
 
0.8%
Decimal Number94
 
0.5%
Dash Punctuation50
 
0.3%
Currency Symbol4
 
< 0.1%
Math Symbol1
 
< 0.1%
Final Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1744
12.2%
a1709
11.9%
i1388
9.7%
n1181
 
8.2%
r1038
 
7.2%
o973
 
6.8%
l958
 
6.7%
s845
 
5.9%
t649
 
4.5%
y508
 
3.5%
Other values (21)3356
23.4%
Uppercase Letter
ValueCountFrequency (%)
S308
 
8.1%
C282
 
7.4%
B281
 
7.4%
M269
 
7.0%
T259
 
6.8%
D251
 
6.6%
A234
 
6.1%
J224
 
5.9%
L213
 
5.6%
P187
 
4.9%
Other values (19)1313
34.4%
Decimal Number
ValueCountFrequency (%)
527
28.7%
217
18.1%
313
13.8%
111
11.7%
010
 
10.6%
95
 
5.3%
44
 
4.3%
63
 
3.2%
72
 
2.1%
82
 
2.1%
Other Punctuation
ValueCountFrequency (%)
.77
51.3%
'24
 
16.0%
!21
 
14.0%
&19
 
12.7%
*4
 
2.7%
"2
 
1.3%
\1
 
0.7%
/1
 
0.7%
,1
 
0.7%
Space Separator
ValueCountFrequency (%)
1513
100.0%
Dash Punctuation
ValueCountFrequency (%)
-50
100.0%
Currency Symbol
ValueCountFrequency (%)
$4
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18170
90.9%
Common1813
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1744
 
9.6%
a1709
 
9.4%
i1388
 
7.6%
n1181
 
6.5%
r1038
 
5.7%
o973
 
5.4%
l958
 
5.3%
s845
 
4.7%
t649
 
3.6%
y508
 
2.8%
Other values (50)7177
39.5%
Common
ValueCountFrequency (%)
1513
83.5%
.77
 
4.2%
-50
 
2.8%
527
 
1.5%
'24
 
1.3%
!21
 
1.2%
&19
 
1.0%
217
 
0.9%
313
 
0.7%
111
 
0.6%
Other values (14)41
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII19950
99.8%
None32
 
0.2%
Punctuation1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1744
 
8.7%
a1709
 
8.6%
1513
 
7.6%
i1388
 
7.0%
n1181
 
5.9%
r1038
 
5.2%
o973
 
4.9%
l958
 
4.8%
s845
 
4.2%
t649
 
3.3%
Other values (65)7952
39.9%
None
ValueCountFrequency (%)
é19
59.4%
ë3
 
9.4%
ó3
 
9.4%
ý2
 
6.2%
Ø2
 
6.2%
Ö1
 
3.1%
í1
 
3.1%
Ü1
 
3.1%
Punctuation
ValueCountFrequency (%)
1
100.0%

song
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1879
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Sorry
 
5
Don't
 
3
Closer
 
3
Breathe
 
3
It's My Life
 
3
Other values (1874)
1983 

Length

Max length114
Median length64
Mean length17.6115
Min length1

Characters and Unicode

Total characters35223
Distinct characters107
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1770 ?
Unique (%)88.5%

Sample

1st rowOops!...I Did It Again
2nd rowAll The Small Things
3rd rowBreathe
4th rowIt's My Life
5th rowBye Bye Bye

Common Values

ValueCountFrequency (%)
Sorry5
 
0.2%
Don't3
 
0.1%
Closer3
 
0.1%
Breathe3
 
0.1%
It's My Life3
 
0.1%
Rise3
 
0.1%
Mercy3
 
0.1%
Faded3
 
0.1%
Higher3
 
0.1%
I Like It3
 
0.1%
Other values (1869)1968
98.4%

Length

2022-11-14T18:09:07.052727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
feat287
 
4.2%
266
 
3.9%
the174
 
2.5%
you159
 
2.3%
me130
 
1.9%
i105
 
1.5%
it99
 
1.4%
love97
 
1.4%
radio95
 
1.4%
edit84
 
1.2%
Other values (2157)5404
78.3%

Most occurring characters

ValueCountFrequency (%)
4900
 
13.9%
e3089
 
8.8%
a2152
 
6.1%
o2135
 
6.1%
t1828
 
5.2%
i1800
 
5.1%
n1512
 
4.3%
r1307
 
3.7%
l997
 
2.8%
s920
 
2.6%
Other values (97)14583
41.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22029
62.5%
Uppercase Letter6291
 
17.9%
Space Separator4900
 
13.9%
Other Punctuation825
 
2.3%
Close Punctuation412
 
1.2%
Open Punctuation412
 
1.2%
Dash Punctuation201
 
0.6%
Decimal Number123
 
0.3%
Other Letter13
 
< 0.1%
Currency Symbol10
 
< 0.1%
Other values (3)7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3089
14.0%
a2152
 
9.8%
o2135
 
9.7%
t1828
 
8.3%
i1800
 
8.2%
n1512
 
6.9%
r1307
 
5.9%
l997
 
4.5%
s920
 
4.2%
h844
 
3.8%
Other values (20)5445
24.7%
Uppercase Letter
ValueCountFrequency (%)
M485
 
7.7%
T480
 
7.6%
S478
 
7.6%
L438
 
7.0%
I385
 
6.1%
B368
 
5.8%
W334
 
5.3%
R330
 
5.2%
D309
 
4.9%
A296
 
4.7%
Other values (19)2388
38.0%
Other Punctuation
ValueCountFrequency (%)
.378
45.8%
'223
27.0%
&86
 
10.4%
,52
 
6.3%
"27
 
3.3%
?17
 
2.1%
!16
 
1.9%
*11
 
1.3%
/9
 
1.1%
#2
 
0.2%
Other values (4)4
 
0.5%
Other Letter
ValueCountFrequency (%)
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
Other values (3)3
23.1%
Decimal Number
ValueCountFrequency (%)
235
28.5%
123
18.7%
019
15.4%
312
 
9.8%
67
 
5.7%
77
 
5.7%
86
 
4.9%
95
 
4.1%
45
 
4.1%
54
 
3.3%
Close Punctuation
ValueCountFrequency (%)
)404
98.1%
]8
 
1.9%
Open Punctuation
ValueCountFrequency (%)
(404
98.1%
[8
 
1.9%
Other Symbol
ValueCountFrequency (%)
®1
50.0%
°1
50.0%
Space Separator
ValueCountFrequency (%)
4900
100.0%
Dash Punctuation
ValueCountFrequency (%)
-201
100.0%
Currency Symbol
ValueCountFrequency (%)
$10
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
+2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28320
80.4%
Common6890
 
19.6%
Katakana8
 
< 0.1%
Hangul5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3089
 
10.9%
a2152
 
7.6%
o2135
 
7.5%
t1828
 
6.5%
i1800
 
6.4%
n1512
 
5.3%
r1307
 
4.6%
l997
 
3.5%
s920
 
3.2%
h844
 
3.0%
Other values (49)11736
41.4%
Common
ValueCountFrequency (%)
4900
71.1%
)404
 
5.9%
(404
 
5.9%
.378
 
5.5%
'223
 
3.2%
-201
 
2.9%
&86
 
1.2%
,52
 
0.8%
235
 
0.5%
"27
 
0.4%
Other values (25)180
 
2.6%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35191
99.9%
None15
 
< 0.1%
Katakana9
 
< 0.1%
Hangul5
 
< 0.1%
Punctuation3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4900
 
13.9%
e3089
 
8.8%
a2152
 
6.1%
o2135
 
6.1%
t1828
 
5.2%
i1800
 
5.1%
n1512
 
4.3%
r1307
 
3.7%
l997
 
2.8%
s920
 
2.6%
Other values (73)14551
41.3%
None
ValueCountFrequency (%)
é3
20.0%
ó3
20.0%
Ø2
13.3%
á2
13.3%
É1
 
6.7%
Ü1
 
6.7%
ñ1
 
6.7%
®1
 
6.7%
°1
 
6.7%
Punctuation
ValueCountFrequency (%)
3
100.0%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Katakana
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

duration_ms
Real number (ℝ≥0)

Distinct1793
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228748.1245
Minimum113000
Maximum484146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:07.514889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum113000
5-th percentile174958.65
Q1203580
median223279.5
Q3248133
95-th percentile298857.35
Maximum484146
Range371146
Interquartile range (IQR)44553

Descriptive statistics

Standard deviation39136.56901
Coefficient of variation (CV)0.171090229
Kurtosis3.314898375
Mean228748.1245
Median Absolute Deviation (MAD)21667
Skewness1.018924709
Sum457496249
Variance1531671034
MonotonicityNot monotonic
2022-11-14T18:09:07.928180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2121064
 
0.2%
2400403
 
0.1%
2075063
 
0.1%
2020663
 
0.1%
2435333
 
0.1%
2688663
 
0.1%
2495333
 
0.1%
2361333
 
0.1%
2290803
 
0.1%
1994803
 
0.1%
Other values (1783)1969
98.5%
ValueCountFrequency (%)
1130001
0.1%
1148931
0.1%
1191331
0.1%
1218861
0.1%
1240551
0.1%
1264461
0.1%
1279201
0.1%
1292641
0.1%
1310641
0.1%
1312131
0.1%
ValueCountFrequency (%)
4841461
0.1%
4529061
0.1%
4485731
0.1%
4443331
0.1%
4321461
0.1%
4179201
0.1%
4041061
0.1%
3938131
0.1%
3667331
0.1%
3599731
0.1%

explicit
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1449 
True
551 
ValueCountFrequency (%)
False1449
72.5%
True551
 
27.6%
2022-11-14T18:09:08.427048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

year
Real number (ℝ≥0)

Distinct23
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.494
Minimum1998
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:08.709944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile2000
Q12004
median2010
Q32015
95-th percentile2018
Maximum2020
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.859960202
Coefficient of variation (CV)0.002916137198
Kurtosis-1.195355107
Mean2009.494
Median Absolute Deviation (MAD)5
Skewness-0.04620500473
Sum4018988
Variance34.33913357
MonotonicityNot monotonic
2022-11-14T18:09:09.047896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2012115
 
5.8%
2017111
 
5.5%
2001108
 
5.4%
2018107
 
5.3%
2010107
 
5.3%
2014104
 
5.2%
2005104
 
5.2%
201199
 
5.0%
201699
 
5.0%
201599
 
5.0%
Other values (13)947
47.3%
ValueCountFrequency (%)
19981
 
0.1%
199938
 
1.9%
200074
3.7%
2001108
5.4%
200290
4.5%
200397
4.9%
200496
4.8%
2005104
5.2%
200695
4.8%
200794
4.7%
ValueCountFrequency (%)
20203
 
0.1%
201989
4.5%
2018107
5.3%
2017111
5.5%
201699
5.0%
201599
5.0%
2014104
5.2%
201389
4.5%
2012115
5.8%
201199
5.0%

popularity
Real number (ℝ≥0)

ZEROS

Distinct76
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.8725
Minimum0
Maximum89
Zeros126
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:09.462826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q156
median65.5
Q373
95-th percentile80
Maximum89
Range89
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.33557703
Coefficient of variation (CV)0.3563501947
Kurtosis2.658744782
Mean59.8725
Median Absolute Deviation (MAD)8.5
Skewness-1.824422019
Sum119745
Variance455.2068472
MonotonicityNot monotonic
2022-11-14T18:09:09.890834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0126
 
6.3%
6976
 
3.8%
6875
 
3.8%
7369
 
3.5%
7469
 
3.5%
6766
 
3.3%
7664
 
3.2%
6463
 
3.1%
6362
 
3.1%
7262
 
3.1%
Other values (66)1268
63.4%
ValueCountFrequency (%)
0126
6.3%
131
 
1.6%
211
 
0.5%
35
 
0.2%
44
 
0.2%
61
 
0.1%
71
 
0.1%
81
 
0.1%
111
 
0.1%
161
 
0.1%
ValueCountFrequency (%)
891
 
0.1%
881
 
0.1%
871
 
0.1%
864
 
0.2%
857
 
0.4%
8411
 
0.5%
8315
 
0.8%
8225
1.2%
8127
1.4%
8043
2.1%

danceability
Real number (ℝ≥0)

Distinct565
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6674375
Minimum0.129
Maximum0.975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:10.303461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.129
5-th percentile0.4199
Q10.581
median0.676
Q30.764
95-th percentile0.886
Maximum0.975
Range0.846
Interquartile range (IQR)0.183

Descriptive statistics

Standard deviation0.1404164146
Coefficient of variation (CV)0.2103813684
Kurtosis0.1255343199
Mean0.6674375
Median Absolute Deviation (MAD)0.091
Skewness-0.4280298559
Sum1334.875
Variance0.01971676948
MonotonicityNot monotonic
2022-11-14T18:09:10.706711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.73612
 
0.6%
0.68812
 
0.6%
0.68711
 
0.5%
0.68211
 
0.5%
0.79111
 
0.5%
0.6610
 
0.5%
0.73310
 
0.5%
0.65610
 
0.5%
0.6810
 
0.5%
0.79410
 
0.5%
Other values (555)1893
94.7%
ValueCountFrequency (%)
0.1291
0.1%
0.1771
0.1%
0.1791
0.1%
0.181
0.1%
0.191
0.1%
0.2091
0.1%
0.2171
0.1%
0.231
0.1%
0.2561
0.1%
0.2591
0.1%
ValueCountFrequency (%)
0.9751
0.1%
0.971
0.1%
0.9691
0.1%
0.9671
0.1%
0.9642
0.1%
0.9632
0.1%
0.9621
0.1%
0.9561
0.1%
0.9551
0.1%
0.9511
0.1%

energy
Real number (ℝ≥0)

HIGH CORRELATION

Distinct580
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.720366
Minimum0.0549
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:11.394487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0549
5-th percentile0.44295
Q10.622
median0.736
Q30.839
95-th percentile0.93305
Maximum0.999
Range0.9441
Interquartile range (IQR)0.217

Descriptive statistics

Standard deviation0.1527452844
Coefficient of variation (CV)0.2120384422
Kurtosis0.1764537256
Mean0.720366
Median Absolute Deviation (MAD)0.108
Skewness-0.6328957919
Sum1440.732
Variance0.02333112191
MonotonicityNot monotonic
2022-11-14T18:09:11.780908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.78315
 
0.8%
0.86212
 
0.6%
0.76811
 
0.5%
0.810
 
0.5%
0.79110
 
0.5%
0.6289
 
0.4%
0.8619
 
0.4%
0.7869
 
0.4%
0.9219
 
0.4%
0.6779
 
0.4%
Other values (570)1897
94.8%
ValueCountFrequency (%)
0.05491
0.1%
0.05811
0.1%
0.1151
0.1%
0.2031
0.1%
0.2191
0.1%
0.2471
0.1%
0.2491
0.1%
0.2611
0.1%
0.2641
0.1%
0.2651
0.1%
ValueCountFrequency (%)
0.9991
 
0.1%
0.9881
 
0.1%
0.9851
 
0.1%
0.9841
 
0.1%
0.9821
 
0.1%
0.9811
 
0.1%
0.9791
 
0.1%
0.9781
 
0.1%
0.9772
0.1%
0.9763
0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.378
Minimum0
Maximum11
Zeros198
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:12.101244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.615058827
Coefficient of variation (CV)0.6721939062
Kurtosis-1.297833309
Mean5.378
Median Absolute Deviation (MAD)3
Skewness-0.009379280142
Sum10756
Variance13.06865033
MonotonicityNot monotonic
2022-11-14T18:09:12.400394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1267
13.4%
11199
10.0%
0198
9.9%
7197
9.8%
5182
9.1%
8173
8.6%
2158
7.9%
9157
7.8%
6154
7.7%
10129
6.5%
Other values (2)186
9.3%
ValueCountFrequency (%)
0198
9.9%
1267
13.4%
2158
7.9%
360
 
3.0%
4126
6.3%
5182
9.1%
6154
7.7%
7197
9.8%
8173
8.6%
9157
7.8%
ValueCountFrequency (%)
11199
10.0%
10129
6.5%
9157
7.8%
8173
8.6%
7197
9.8%
6154
7.7%
5182
9.1%
4126
6.3%
360
 
3.0%
2158
7.9%

loudness
Real number (ℝ)

HIGH CORRELATION

Distinct1671
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.5124345
Minimum-20.514
Maximum-0.276
Zeros0
Zeros (%)0.0%
Negative2000
Negative (%)100.0%
Memory size15.8 KiB
2022-11-14T18:09:12.751647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-20.514
5-th percentile-8.9244
Q1-6.49025
median-5.285
Q3-4.16775
95-th percentile-2.9415
Maximum-0.276
Range20.238
Interquartile range (IQR)2.3225

Descriptive statistics

Standard deviation1.933482013
Coefficient of variation (CV)-0.3507492041
Kurtosis3.959788496
Mean-5.5124345
Median Absolute Deviation (MAD)1.1535
Skewness-1.199866094
Sum-11024.869
Variance3.738352696
MonotonicityNot monotonic
2022-11-14T18:09:13.167520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.3665
 
0.2%
-5.5954
 
0.2%
-5.1534
 
0.2%
-3.7824
 
0.2%
-5.6113
 
0.1%
-5.8923
 
0.1%
-4.3153
 
0.1%
-3.0783
 
0.1%
-5.5763
 
0.1%
-4.6993
 
0.1%
Other values (1661)1965
98.2%
ValueCountFrequency (%)
-20.5141
0.1%
-17.2171
0.1%
-15.6361
0.1%
-14.5051
0.1%
-13.9641
0.1%
-13.7441
0.1%
-13.6091
0.1%
-13.41
0.1%
-13.2031
0.1%
-13.21
0.1%
ValueCountFrequency (%)
-0.2761
0.1%
-0.741
0.1%
-1.1311
0.1%
-1.191
0.1%
-1.2031
0.1%
-1.2311
0.1%
-1.2991
0.1%
-1.3571
0.1%
-1.5381
0.1%
-1.5691
0.1%

mode
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1107 
0
893 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
11107
55.4%
0893
44.6%

Length

2022-11-14T18:09:13.563158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-14T18:09:13.960320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
11107
55.4%
0893
44.6%

Most occurring characters

ValueCountFrequency (%)
11107
55.4%
0893
44.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11107
55.4%
0893
44.6%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11107
55.4%
0893
44.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11107
55.4%
0893
44.6%

speechiness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct837
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1035677
Minimum0.0232
Maximum0.576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:14.263909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.029
Q10.0396
median0.05985
Q30.129
95-th percentile0.322
Maximum0.576
Range0.5528
Interquartile range (IQR)0.0894

Descriptive statistics

Standard deviation0.09615876419
Coefficient of variation (CV)0.928462872
Kurtosis2.626100136
Mean0.1035677
Median Absolute Deviation (MAD)0.02645
Skewness1.761876968
Sum207.1354
Variance0.009246507931
MonotonicityNot monotonic
2022-11-14T18:09:14.664890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.043213
 
0.7%
0.02912
 
0.6%
0.032211
 
0.5%
0.036310
 
0.5%
0.1099
 
0.4%
0.04399
 
0.4%
0.03778
 
0.4%
0.1088
 
0.4%
0.0468
 
0.4%
0.04318
 
0.4%
Other values (827)1904
95.2%
ValueCountFrequency (%)
0.02321
0.1%
0.02391
0.1%
0.02411
0.1%
0.02421
0.1%
0.02452
0.1%
0.02471
0.1%
0.02492
0.1%
0.02522
0.1%
0.02532
0.1%
0.02552
0.1%
ValueCountFrequency (%)
0.5761
0.1%
0.531
0.1%
0.5161
0.1%
0.5051
0.1%
0.4881
0.1%
0.4841
0.1%
0.4831
0.1%
0.4781
0.1%
0.471
0.1%
0.4671
0.1%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1208
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.128954928
Minimum1.92 × 10-5
Maximum0.976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:15.107543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.92 × 10-5
5-th percentile0.0009853
Q10.014
median0.0557
Q30.17625
95-th percentile0.515
Maximum0.976
Range0.9759808
Interquartile range (IQR)0.16225

Descriptive statistics

Standard deviation0.1733459262
Coefficient of variation (CV)1.34423654
Kurtosis4.665975534
Mean0.128954928
Median Absolute Deviation (MAD)0.05101
Skewness2.094133723
Sum257.9098559
Variance0.03004881014
MonotonicityNot monotonic
2022-11-14T18:09:15.503559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.237
 
0.4%
0.1077
 
0.4%
0.157
 
0.4%
0.1916
 
0.3%
0.1576
 
0.3%
0.1026
 
0.3%
0.1096
 
0.3%
0.01926
 
0.3%
0.1036
 
0.3%
0.1236
 
0.3%
Other values (1198)1937
96.9%
ValueCountFrequency (%)
1.92 × 10-51
0.1%
2.06 × 10-51
0.1%
2.64 × 10-51
0.1%
3.82 × 10-51
0.1%
4.14 × 10-51
0.1%
5.15 × 10-51
0.1%
5.48 × 10-51
0.1%
6.52 × 10-51
0.1%
6.79 × 10-51
0.1%
7.95 × 10-51
0.1%
ValueCountFrequency (%)
0.9761
0.1%
0.9661
0.1%
0.9531
0.1%
0.9451
0.1%
0.9342
0.1%
0.9321
0.1%
0.9221
0.1%
0.8961
0.1%
0.8931
0.1%
0.8831
0.1%

instrumentalness
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct772
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0152259906
Minimum0
Maximum0.985
Zeros1087
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:15.902292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36.83 × 10-5
95-th percentile0.034505
Maximum0.985
Range0.985
Interquartile range (IQR)6.83 × 10-5

Descriptive statistics

Standard deviation0.0877707216
Coefficient of variation (CV)5.764532757
Kurtosis61.46993824
Mean0.0152259906
Median Absolute Deviation (MAD)0
Skewness7.581735568
Sum30.45198121
Variance0.00770369957
MonotonicityNot monotonic
2022-11-14T18:09:16.307156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01087
54.4%
0.00133
 
0.1%
0.0001083
 
0.1%
1.6 × 10-63
 
0.1%
0.0001393
 
0.1%
1.96 × 10-63
 
0.1%
0.0001133
 
0.1%
8.83 × 10-63
 
0.1%
2.77 × 10-63
 
0.1%
0.0001573
 
0.1%
Other values (762)886
44.3%
ValueCountFrequency (%)
01087
54.4%
1.01 × 10-61
 
0.1%
1.03 × 10-63
 
0.1%
1.04 × 10-61
 
0.1%
1.07 × 10-61
 
0.1%
1.1 × 10-61
 
0.1%
1.11 × 10-63
 
0.1%
1.13 × 10-61
 
0.1%
1.16 × 10-62
 
0.1%
1.2 × 10-61
 
0.1%
ValueCountFrequency (%)
0.9851
0.1%
0.9251
0.1%
0.9011
0.1%
0.8941
0.1%
0.8281
0.1%
0.8121
0.1%
0.8091
0.1%
0.7991
0.1%
0.7921
0.1%
0.7511
0.1%

liveness
Real number (ℝ≥0)

Distinct783
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1812158
Minimum0.0215
Maximum0.853
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:16.726687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0215
5-th percentile0.051595
Q10.0881
median0.124
Q30.241
95-th percentile0.4603
Maximum0.853
Range0.8315
Interquartile range (IQR)0.1529

Descriptive statistics

Standard deviation0.1406692067
Coefficient of variation (CV)0.776252439
Kurtosis3.831586847
Mean0.1812158
Median Absolute Deviation (MAD)0.05055
Skewness1.848579144
Sum362.4316
Variance0.01978782572
MonotonicityNot monotonic
2022-11-14T18:09:17.118798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10425
 
1.2%
0.11123
 
1.1%
0.10720
 
1.0%
0.10819
 
0.9%
0.11819
 
0.9%
0.11218
 
0.9%
0.10617
 
0.9%
0.10516
 
0.8%
0.10115
 
0.8%
0.12415
 
0.8%
Other values (773)1813
90.6%
ValueCountFrequency (%)
0.02151
0.1%
0.02341
0.1%
0.02411
0.1%
0.02631
0.1%
0.02721
0.1%
0.0281
0.1%
0.02832
0.1%
0.02861
0.1%
0.02882
0.1%
0.0291
0.1%
ValueCountFrequency (%)
0.8531
0.1%
0.8431
0.1%
0.8391
0.1%
0.8331
0.1%
0.8261
0.1%
0.821
0.1%
0.8171
0.1%
0.8011
0.1%
0.7951
0.1%
0.7751
0.1%

valence
Real number (ℝ≥0)

Distinct760
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55168965
Minimum0.0381
Maximum0.973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:17.517947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0381
5-th percentile0.1739
Q10.38675
median0.5575
Q30.73
95-th percentile0.89505
Maximum0.973
Range0.9349
Interquartile range (IQR)0.34325

Descriptive statistics

Standard deviation0.2208641898
Coefficient of variation (CV)0.4003413691
Kurtosis-0.8218785176
Mean0.55168965
Median Absolute Deviation (MAD)0.1715
Skewness-0.1288158625
Sum1103.3793
Variance0.04878099033
MonotonicityNot monotonic
2022-11-14T18:09:17.878046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.41811
 
0.5%
0.749
 
0.4%
0.48
 
0.4%
0.4468
 
0.4%
0.8018
 
0.4%
0.5278
 
0.4%
0.87
 
0.4%
0.6547
 
0.4%
0.3527
 
0.4%
0.5547
 
0.4%
Other values (750)1920
96.0%
ValueCountFrequency (%)
0.03811
0.1%
0.04061
0.1%
0.05941
0.1%
0.05961
0.1%
0.06811
0.1%
0.06941
0.1%
0.07561
0.1%
0.07831
0.1%
0.07841
0.1%
0.07891
0.1%
ValueCountFrequency (%)
0.9732
 
0.1%
0.9722
 
0.1%
0.9691
 
0.1%
0.9681
 
0.1%
0.9664
0.2%
0.9653
0.1%
0.9644
0.2%
0.9632
 
0.1%
0.9625
0.2%
0.9614
0.2%

tempo
Real number (ℝ≥0)

Distinct1831
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.1225575
Minimum60.019
Maximum210.851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2022-11-14T18:09:18.624219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum60.019
5-th percentile80.52815
Q198.98575
median120.0215
Q3134.2655
95-th percentile172.09705
Maximum210.851
Range150.832
Interquartile range (IQR)35.27975

Descriptive statistics

Standard deviation26.96711199
Coefficient of variation (CV)0.224496652
Kurtosis0.02296970553
Mean120.1225575
Median Absolute Deviation (MAD)19.8895
Skewness0.5467700722
Sum240245.115
Variance727.225129
MonotonicityNot monotonic
2022-11-14T18:09:19.029196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140.0224
 
0.2%
97.9543
 
0.1%
83.0663
 
0.1%
127.9993
 
0.1%
120.0033
 
0.1%
127.9853
 
0.1%
121.9963
 
0.1%
128.0083
 
0.1%
91.033
 
0.1%
143.9943
 
0.1%
Other values (1821)1969
98.5%
ValueCountFrequency (%)
60.0191
0.1%
62.8761
0.1%
64.9341
0.1%
65.0431
0.1%
65.9972
0.1%
68.5071
0.1%
68.6371
0.1%
68.9421
0.1%
68.9761
0.1%
70.5431
0.1%
ValueCountFrequency (%)
210.8511
0.1%
205.571
0.1%
203.9111
0.1%
203.8621
0.1%
202.0151
0.1%
201.9361
0.1%
201.81
0.1%
199.9581
0.1%
199.9351
0.1%
199.7641
0.1%

genre
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct59
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
pop
428 
hip hop, pop
277 
hip hop, pop, R&B
244 
pop, Dance/Electronic
221 
pop, R&B
178 
Other values (54)
652 

Length

Max length37
Median length32
Mean length11.8785
Min length3

Characters and Unicode

Total characters23757
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.9%

Sample

1st rowpop
2nd rowrock, pop
3rd rowpop, country
4th rowrock, metal
5th rowpop

Common Values

ValueCountFrequency (%)
pop428
21.4%
hip hop, pop277
13.9%
hip hop, pop, R&B244
12.2%
pop, Dance/Electronic221
11.1%
pop, R&B178
8.9%
hip hop124
 
6.2%
hip hop, pop, Dance/Electronic78
 
3.9%
rock58
 
2.9%
rock, pop43
 
2.1%
Dance/Electronic41
 
2.1%
Other values (49)308
15.4%

Length

2022-11-14T18:09:19.478653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pop1633
36.4%
hip778
17.3%
hop778
17.3%
r&b452
 
10.1%
dance/electronic390
 
8.7%
rock234
 
5.2%
metal66
 
1.5%
latin64
 
1.4%
set22
 
0.5%
country21
 
0.5%
Other values (7)51
 
1.1%

Most occurring characters

ValueCountFrequency (%)
p4822
20.3%
o3116
13.1%
2489
10.5%
,1704
 
7.2%
h1556
 
6.5%
c1467
 
6.2%
i1287
 
5.4%
n889
 
3.7%
e886
 
3.7%
r665
 
2.8%
Other values (25)4876
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16904
71.2%
Other Punctuation2576
 
10.8%
Space Separator2489
 
10.5%
Uppercase Letter1744
 
7.3%
Close Punctuation22
 
0.1%
Open Punctuation22
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p4822
28.5%
o3116
18.4%
h1556
 
9.2%
c1467
 
8.7%
i1287
 
7.6%
n889
 
5.3%
e886
 
5.2%
r665
 
3.9%
t600
 
3.5%
l573
 
3.4%
Other values (11)1043
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
B452
25.9%
R452
25.9%
E390
22.4%
D390
22.4%
F20
 
1.1%
A20
 
1.1%
W10
 
0.6%
T10
 
0.6%
Other Punctuation
ValueCountFrequency (%)
,1704
66.1%
&452
 
17.5%
/420
 
16.3%
Space Separator
ValueCountFrequency (%)
2489
100.0%
Close Punctuation
ValueCountFrequency (%)
)22
100.0%
Open Punctuation
ValueCountFrequency (%)
(22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18648
78.5%
Common5109
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
p4822
25.9%
o3116
16.7%
h1556
 
8.3%
c1467
 
7.9%
i1287
 
6.9%
n889
 
4.8%
e886
 
4.8%
r665
 
3.6%
t600
 
3.2%
l573
 
3.1%
Other values (19)2787
14.9%
Common
ValueCountFrequency (%)
2489
48.7%
,1704
33.4%
&452
 
8.8%
/420
 
8.2%
)22
 
0.4%
(22
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII23757
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p4822
20.3%
o3116
13.1%
2489
10.5%
,1704
 
7.2%
h1556
 
6.5%
c1467
 
6.2%
i1287
 
5.4%
n889
 
3.7%
e886
 
3.7%
r665
 
2.8%
Other values (25)4876
20.5%

Interactions

2022-11-14T18:08:59.384997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:00.668737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:06.393442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:11.358842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:16.042565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:21.156489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:26.025859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:30.863141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:35.769342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:40.483209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:45.193830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:49.958099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:54.829597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:59.818371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:01.266968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:06.788516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:11.750464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:16.457548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:21.509610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:26.427613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:31.251215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:36.151451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:41.123353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:45.578607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:50.332625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:55.202185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:00.162449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:01.752465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:07.129740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:12.068573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:16.804796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:21.891923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:26.799939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:31.628040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:36.555857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:41.428393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:45.924949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:50.717977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:55.502079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:00.568342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:02.201693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:07.439765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:12.370119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:17.172370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:22.255732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:27.140624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:32.023568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:36.891455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:41.769478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:46.260473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:51.034740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:55.835705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:00.957633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:02.623319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:08.101895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:12.708986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:17.486182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:22.598108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:27.452208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:32.374351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:37.228111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:42.118707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:46.616334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:51.729894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:56.182296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:01.291441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:03.046536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:08.458224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:13.074268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:17.828511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:22.979415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:27.771142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:32.752597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:37.549056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:42.468243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:46.976934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:52.049876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:56.546717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:01.631018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:03.497416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:08.776615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:13.464114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:18.167618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:23.354499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:28.094602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:33.143355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:37.931933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:42.793936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:47.326985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:52.382983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:56.917699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:02.333630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:03.927266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:09.167497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:13.842394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:18.591946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:23.793570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:28.495586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:33.540821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:38.305743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:43.162379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:47.734942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:52.749459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:57.289029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:02.734920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:04.354878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:09.590792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:14.200749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:19.266977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:24.180863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:28.869750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:33.927520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:38.669016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:43.516439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:48.079634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:53.109786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:57.644859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:03.114561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:04.729923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:09.942960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:14.583666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:19.678598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:24.516422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:29.173803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:34.327290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:39.026935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:43.852331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:48.403774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:53.468838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:57.984459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:03.561014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:05.154604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:10.278190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:14.931427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:20.017575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:24.895537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:29.538888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:34.668485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:39.393844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:44.205367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:48.828379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:53.834232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:58.351665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:03.889736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:05.537611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:10.621338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:15.293868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:20.394590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:25.238586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:29.834890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:34.985009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:39.735091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:44.492556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:49.168420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:54.163651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:58.718153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:09:04.242023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:05.957581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:10.971609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:15.673730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:20.772150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:25.611119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:30.485214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:35.361218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:40.098381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:44.858474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:49.539984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:54.468878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-14T18:08:59.065043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-11-14T18:09:19.850687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-14T18:09:20.540258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-14T18:09:21.145352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-14T18:09:21.800363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-14T18:09:22.375723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-14T18:09:22.756737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-14T18:09:04.895163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-14T18:09:05.821895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

artistsongduration_msexplicityearpopularitydanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempogenre
0Britney SpearsOops!...I Did It Again211160False2000770.7510.8341-5.44400.04370.300000.0000180.35500.89495.053pop
1blink-182All The Small Things167066False1999790.4340.8970-4.91810.04880.010300.0000000.61200.684148.726rock, pop
2Faith HillBreathe250546False1999660.5290.4967-9.00710.02900.173000.0000000.25100.278136.859pop, country
3Bon JoviIt's My Life224493False2000780.5510.9130-4.06300.04660.026300.0000130.34700.544119.992rock, metal
4*NSYNCBye Bye Bye200560False2000650.6140.9288-4.80600.05160.040800.0010400.08450.879172.656pop
5SisqoThong Song253733True1999690.7060.8882-6.95910.06540.119000.0000960.07000.714121.549hip hop, pop, R&B
6EminemThe Real Slim Shady284200True2000860.9490.6615-4.24400.05720.030200.0000000.04540.760104.504hip hop
7Robbie WilliamsRock DJ258560False2000680.7080.7727-4.26410.03220.026700.0000000.46700.861103.035pop, rock
8Destiny's ChildSay My Name271333False1999750.7130.6785-3.52500.10200.273000.0000000.14900.734138.009pop, R&B
9ModjoLady - Hear Me Tonight307153False2001770.7200.8086-5.62710.03790.007930.0293000.06340.869126.041Dance/Electronic

Last rows

artistsongduration_msexplicityearpopularitydanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempogenre
1990Sam SmithHow Do You Sleep?202204False2019730.4770.6821-4.93100.09250.15300.0000000.07630.345110.567pop
1991NSGOptions240081True2020570.8360.6211-4.68400.08940.38900.0000920.10400.762101.993World/Traditional, hip hop
1992NormaniMotivation193837False2019710.5990.8874-3.96710.09840.01920.0000010.30000.881170.918pop, R&B
1993Joel CorrySorry188640False2019630.7440.7908-4.61700.05620.05470.0008020.32000.847125.002pop, Dance/Electronic
1994Post MaloneGoodbyes (Feat. Young Thug)174960True201910.5800.6535-3.81810.07450.44700.0000000.11100.175150.231hip hop
1995Jonas BrothersSucker181026False2019790.8420.7341-5.06500.05880.04270.0000000.10600.952137.958pop
1996Taylor SwiftCruel Summer178426False2019780.5520.7029-5.70710.15700.11700.0000210.10500.564169.994pop
1997Blanco BrownThe Git Up200593False2019690.8470.6789-8.63510.10900.06690.0000000.27400.81197.984hip hop, country
1998Sam SmithDancing With A Stranger (with Normani)171029False2019750.7410.5208-7.51310.06560.45000.0000020.22200.347102.998pop
1999Post MaloneCircles215280False2019850.6950.7620-3.49710.03950.19200.0024400.08630.553120.042hip hop

Duplicate rows

Most frequently occurring

artistsongduration_msexplicityearpopularitydanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempogenre# duplicates
0Ariana GrandeLove Me Harder236133False2014740.4720.7141-4.38900.03340.009370.0000000.07640.24098.992pop2
1Baby BashSuga Suga239026False2003730.6620.7485-3.04100.26800.688000.0000080.08410.53582.331hip hop, pop, R&B, latin2
2Billie Eilishlovely (with Khalid)200185False2018860.3510.2964-10.10900.03330.934000.0000000.09500.120115.284pop, Dance/Electronic2
3Bruno MarsLocked out of Heaven233478False2012850.7260.6985-4.16510.04310.049000.0000000.30900.867143.994pop2
4Bryson TillerDon't198293True2015780.7650.35611-5.55600.19500.223000.0000000.09630.18996.991hip hop, pop, R&B2
5Busta RhymesI Know What You Want (feat. Flipmode Squad)324306True2002680.6480.7596-4.31510.30600.014200.0000000.64800.51885.996hip hop, pop2
6Chris BrownKiss Kiss (feat. T-Pain)250666False2007680.7290.65810-3.38600.22500.050600.0000000.06930.551140.043hip hop, pop, R&B2
7ColdplayClocks307879False2002790.5770.7495-7.21500.02790.599000.0115000.18300.255130.970rock, pop2
8ColdplayParadise278719False2011820.4490.5855-6.76110.02680.050900.0000870.08330.212139.631rock, pop2
9Craig DavidFill Me In257200False2000600.6820.7448-6.98110.03650.376000.0095100.06000.827132.493hip hop, pop, R&B2